Contents

Analyse Rapide des notes

import pandas as pd
from ipywidgets import interact, interactive, fixed, interact_manual, IntSlider
# Standard plotly imports
import chart_studio.plotly as py
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
# Using plotly + cufflinks in offline mode
import cufflinks as cf
cf.go_offline(connected=False)
init_notebook_mode(connected=False)

Continus

DS 1

df=pd.read_csv("Notes_DSC1.csv", encoding="latin-1")

df.describe()
Note Exo1 Exo2 Exo 3
count 28.000000 28.000000 28.000000 28.000000
mean 12.660714 3.401786 4.026786 5.000000
std 3.609539 1.198648 1.774745 1.635826
min 4.500000 0.750000 0.500000 0.750000
25% 10.625000 3.000000 2.937500 4.437500
50% 13.000000 3.875000 4.250000 5.500000
75% 15.250000 4.250000 5.062500 6.062500
max 18.250000 4.750000 7.250000 7.000000 
df['Note']=df["Note"]*100/20

df['Exo1'] = df['Exo1']*100/5
df['Exo2'] = df['Exo2']*100/8
df['Exo 3'] = df['Exo 3']*100/7

df["Note"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo1"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exercice 1 - Calculs matriciel",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo2"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 2 - Géométrie",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo 3"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 3 - Matrices, inversion",xTitle='Notes sur 100', yTitle='Quantité')

DS 2

df=pd.read_csv("Notes_DSC2.csv", encoding="latin-1")

df.describe()
Note Exo1 Exo2 Exo 3 Exo 4
count 22.000000 22.000000 22.000000 22.000000 22.000000
mean 9.750000 5.034091 0.704545 2.250000 1.625000
std 2.928717 1.188475 0.868210 0.893095 1.475494
min 3.500000 2.250000 0.000000 0.000000 0.000000
25% 8.250000 4.500000 0.000000 2.000000 0.250000
50% 9.375000 5.250000 0.500000 2.375000 1.625000
75% 11.375000 6.000000 1.000000 3.000000 2.000000
max 16.000000 6.500000 3.000000 3.500000 5.000000 
df['Note']=df["Note"]*100/20

df['Exo1'] = df['Exo1']*100/7
df['Exo2'] = df['Exo2']*100/4
df['Exo 3'] = df['Exo 3']*100/4
df['Exo 4'] = df['Exo 4']*100/5

df["Note"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo1"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exercice 1 - Statistiques",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo2"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 2 - Dénombrement et probabilités conditionnelles",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo 3"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 3 - Loi binomiale",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo 4"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 4 - Loi normale",xTitle='Notes sur 100', yTitle='Quantité')

Alternants

DS 1

df=pd.read_csv("Notes_DSA1.csv", encoding="latin-1")

df.describe()
Note Exo1 Exo2 Exo 3
count 22.000000 22.000000 22.000000 22.000000
mean 11.681818 2.579545 3.625000 5.340909
std 3.132189 0.853516 1.873611 1.493130
min 6.000000 1.000000 0.000000 1.500000
25% 9.562500 2.125000 2.437500 4.312500
50% 11.750000 2.500000 3.750000 5.500000
75% 13.187500 3.187500 4.437500 6.500000
max 19.000000 4.000000 8.000000 7.000000 
df['Note']=df["Note"]*100/20

df['Exo1'] = df['Exo1']*100/5
df['Exo2'] = df['Exo2']*100/8
df['Exo 3'] = df['Exo 3']*100/7

df["Note"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo1"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exercice 1 - Calculs matriciel",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo2"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 2 - Géométrie",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo 3"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 3 - Matrices, inversion",xTitle='Notes sur 100', yTitle='Quantité')

DS 2

df=pd.read_csv("Notes_DSA2.csv", encoding="latin-1")

df.describe()
Note Exo1 Exo2 Exo 3 Exo 4
count 21.000000 21.000000 21.000000 21.000000 21.000000
mean 11.345238 5.761905 0.654762 2.333333 2.488095
std 3.390682 0.878479 0.808069 1.130081 1.701977
min 6.250000 3.500000 0.000000 0.000000 0.000000
25% 9.000000 5.500000 0.000000 1.750000 1.000000
50% 11.250000 6.000000 0.500000 2.500000 2.000000
75% 13.000000 6.250000 1.000000 3.000000 4.000000
max 19.500000 7.000000 2.750000 4.000000 5.000000 
df['Note']=df["Note"]*100/20

df['Exo1'] = df['Exo1']*100/7
df['Exo2'] = df['Exo2']*100/4
df['Exo 3'] = df['Exo 3']*100/4
df['Exo 4'] = df['Exo 4']*100/5

df["Note"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo1"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exercice 1 - Statistiques",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo2"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 2 - Dénombrement et proba conditionnelles",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo 3"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 3 - Loi binomiale",xTitle='Notes sur 100', yTitle='Quantité')

df["Exo 4"].iplot(kind="histogram", bins=20, theme="white", title="Répartition des notes - Exo 4 - Loi normale",xTitle='Notes sur 100', yTitle='Quantité')